Yet at the same time, a software pioneer tells us that general superhuman artificial intelligence is very unlikely because neither we nor machines can design something smarter than ourselves. Also, no combination of random and deterministic processing can increase mutual information (Levin’s Law).

One area affected by the controversy is the arts, for example. AI in its present form, clearly cannot write better novels and screenplays than people. But what about painting? Software engineer Ben Dicksonsays no, not painting either.

He also thinks that the nature of the problem is often misunderstood. For example, he offers some thoughts on the AI-produced portrait created by three French students using borrowed code that sold for $432,500 at a recent Christies’ art auction. Rather than announce that human artists are now doomed, Dixon interviewed a number of them and came away with a rather different picture, that “AI-generated art will improve, but artistic creativity will remain a human discipline.” Why?

“When you’re drawing a painting, composing a song, writing a novel (or even this blog post), your life experiences, culture, religion, political and social tendencies all mix into a jumble of emotions and chemical reactions that affect the result of your work. A real analysis of what goes into human creativity is beyond my knowledge or a single post. Suffice it to say that we can’t truly understand the human creativity process and every single work of human art is unique in its own right. Trying to reproduce it would effectively be like trying to step in the same river twice.” Even if it were possible, is it worth doing? Would an AI’s simulated experiences matter?

How does machine learning create art anyway?

At the heart of most recent AI innovations are neural networks, complex structures that are especially good at examining and matching patterns and classifying information. The deep learning techniques used in various art and music generation tools differ, but one specific technology that has become very popular is generative adversarial networks (GAN). GANs involve two neural networks, one that generates new data and a second one that evaluates the first one’s output to see if it passes for a specific class of data. Ben Dickson, “Deep learning is not a replacement for human creativity, period” at TechTalks

The output of such a system, refined and run often enough, will eventually produce a marketable item that, in a frame, will look like art to somebody. But most artists have more to communicate than that, whether they do it well or badly.

However, Dixon thinks Deep Learning can augment genuine human creativity: “For instance, neural networks can take a drawing and modify it to give it a Van Gogh or Picasso style. Another example is a tool developed by Google that uses machine learning to examine rough sketches and transform them into crisp drawings.” That raises a question, of course, whether a fill-in-the-blanks approach will stifle more creativity than it enhances. At some point, the artist must turn it all off and look deeper within, however challenging he finds the view.

The huge price tag for the AI art reminds us that what things sell for is not necessarily a function of the creativity involved: Elephant art,chimpanzee art, and found art can all fetch significant prices for a variety of reasons, including philanthropic, political, or historical ones. Creativity is not always a significant factor in determining the price.

As it happens, many of the ideas around AI-generated art are not brand new. In the 1940s, George Orwell (1903–1950) thought that a machine could write popular novels that did not require creative thinking and in the 1960s, computer visionaries made an experimental film with AI generating a short Western (cowboy story). That experiment and a recent attempt at sci-fi show that the product means something to the viewer to be viable and, generally speaking, machines don’t generate meaning on their own. We will need to support our local creative communities for that.

Artists can instantiate their ideas more efficiently using better tools. Michelangelo could be more precise than the Stone Age cave artists. But artists can’t just use AI to automate creativity so that the machine writes masterpieces while they doze off. Information does not create and arrange itself via magic.

Mind Matters features original news and analysis at the intersection of artificial and natural intelligence. Through articles and podcasts, it explores issues, challenges, and controversies relating to human and artificial intelligence from a perspective that values the unique capabilities of human beings. Mind Matters is published by the Walter Bradley Center for Natural and Artificial Intelligence.